SoroushVahidi/scidocs huggingface dataset:
A Novel Design of $4 \times 4$ Butler Matrix With Relatively Flexible Phase Differences
(2016)

This letter presents a novel topology of a 4 ×4 Butler matrix, which can realize relatively flexible phase differences at the output ports. The proposed Butler matrix employs couplers with arbitrary phase-differences to replace quadrature couplers in the conventional Butler matrix. By controlling the phase differences of the applied couplers, the progressive phase differences among output ports of the proposed Butler matrix can be relatively flexible. To facilitate the design, closed-form design equations are derived and presented. For verifying the design concept, a planar 4×4 Butler matrix with four unique progressive phase differences ( - 30<sup>°</sup>, + 150<sup>°</sup>, - 120<sup>°</sup>, and + 60<sup>°</sup>) is designed and fabricated. At the operating frequency, the amplitude imbalance is less than 0.75 dB, and the phase mismatch is within ±6<sup>°</sup>. The measured return loss is better than 16 dB, and the isolation is better than 18 dB. The bandwidth with 10 dB return loss is about 15%.
Linked REST APIs: A Middleware for Semantic REST API Integration
(2017)

Over the last decade, an exponentially increasing number of REST services have been providing a simple and straightforward syntax for accessing rich data resources. To use these services, however, developers have to understand "information-use contracts" specified in natural language, and, to build applications that benefit from multiple existing services they have to map the underlying resource schemas in their code. This process is difficult and error-prone, especially as the number and overlap of the underlying services increases, and the mappings become opaque, difficult to maintain, and practically impossible to reuse. The more recent advent of the Linked Data formalisms can offer a solution to the challenge. In this paper, we propose a conceptual framework for REST-service integration based on Linked Data models. In this framework, the data exposed by REST services is mapped to Linked Data schemas, based on these descriptions, we have developed a middleware that can automatically compose API calls to respond to data queries (in SPARQL). Furthermore, we have developed a RDF model for characterizing the access-control protocols of these APIs and the quality of the data they expose, so that our middleware can develop "legal" compositions with desired qualities. We report our experience with the implementation of a prototype that demonstrates the usefulness of our framework in the context of a research-data management application.
Cooperation between distributed agents through self-organisation
(1989)

The paper discussu some antral irrw in the cooperation between disuibuted agcnu using lhe following case study: The objective is to explore a distant planet. more concretely to collect samples of a parricular typ of precious ruck. The location of the ruck .iamplu is unknown in advance but they arc rypiwlly clustered in cenain spm There i S a vehicle that can drive around on the planet and later reenter the s p x d (0 g0 back to ewlh There is no deuiled map of the t e m h although it h known hat the terrain is fuU of obstacles. hills. valleys. etc.
Privacy Preserving Data
(2000)

In this paper we introduce the concept of privacy preserving data mining. In our model, two parties owning conndential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. This problem has many practical and important applications, such as in medical research with conndential patient records. Data mining algorithms are usually complex, especially as the size of the input is measured in megabytes, if not gigabytes. A generic secure multi-party computation solution, based on evaluation of a circuit computing the algorithm on the entire input, is therefore of no practical use. We focus on the problem of decision tree learning and use ID3, a popular and widely used algorithm for this problem. We present a solution that is considerably more eecient than generic solutions. It demands very few rounds of communication and reasonable bandwidth. In our solution, each party performs by itself a computation of the same order as computing the ID3 algorithm for its own database. The results are then combined using eecient cryptographic protocols, whose overhead is only logarithmic in the number of transactions in the databases. We feel that our result is a substantial contribution, demonstrating that secure multi-party computation can be made practical, even for complex problems and large inputs.
Gaze-Adaptive Above and On-Surface Interaction
(2017)

We explore the combination of above-surface sensing with eye tracking to facilitate concurrent interaction with multiple regions on touch screens. Conventional touch input relies on positional accuracy, thereby requiring tight visual monitoring of one's own motor action. In contrast, above-surface sensing and eye tracking provides information about how user's hands and gaze are distributed across the interface. In these situations we facilitate interaction by 1) showing the visual feedback of the hand hover near user's gaze point and 2) decrease the requisite of positional accuracy by employing gestural information. We contribute input and visual feedback techniques that combine these modalities and demonstrate their use in example applications. A controlled study showed the effectiveness of our techniques for manipulation tasks against conventional touch, while the effectiveness in acquisition tasks depended on the amount of mid-air motion, leading to our conclusion that the techniques can benefit interacting with multiple interface regions.
Enterprise Content Management (ECM): Needs, challenges and recommendations
(2009)

Availability of information, agility of business process, interoperability of diverse business functions and conformity of the legal requirements are few of the critical success factors for enterprises to survive in the modern and competing global market space. Enterprises while striving to achieve these critical success factors are further exposed to the complexity of massive volumes of variant data and information, which exists in a broad array of formats. Complex and extended business processes spanning across the business functions and partners around the globe, need for integration and interoperability, fulfillment of compliance to legal and regulatory requirements. Enterprise content management (ECM) answers the above questions and offers the capabilities to efficiently and effectively manage the challenges imposed by the demanding business requirements of the modern world. The study in-hand reviews and analyzes the existing literature on enterprise content management domain. In order to conduct a structured literature review, three broader areas of considerations for ECM are identified i.e. business needs & drivers, challenges and recommendations. The review inhand aims at consolidating valuable domain knowledge and research work accomplished in the recent years for the benefits of the industry and academia.
Web Application Testing: A Review on Techniques, Tools and State of Art
(2012)

Web applications are meant to be viewed by human user. While this implies that quality of web application has importance in our daily life. Web application quality is our prime concern. To ensure the quality of web application, web testing is having a dandy role in Software Testing as well as Web Community. Web Applications are erring because of features provided for rising of web application. In the last years, various web testing problems have been addressed by research work. Several tools, techniques and methods have been determined to test w eb application eff icaciously. This paper w ill present the contribution of researchers in the f ield of web application in previous years and state of art of web testing and challenges primarily because of distributed and heterogeneous nature of web
A study of interactive 3D point location in a computer simulated virtual environment
(1997)

The present study investigated the ability to inter actively locate points in a three dimensional computer environment using a six degree of freedom input device. Four different visu al feedback modes were tested: fixed viewpoint monoscopic persp ective, fixed viewpoint stereoscopic perspective, head-tracked mo noscopic perspective and head-tracked stereoscopic perspecti ve. Targets were located at plus and minus 10cm along the X, Y or Z axes, from a fixed starting location. Data about the time to complete the task and positioning accuracy (error) are gathered for each trial. In addition, subjective feedback regarding the apparat us, visual mode and task difficulty was solicited from subject s. The results indicate that stereoscopic performance is superior to monoscopic performance and that asymmetries exist both across and within axes. Head tracking had no appreciable effect upon performance. Subjective feedback regarding performance is usuall y consistent with objective measures, but some inconsistencies a re present.
Pavlov: Programming by Stimulus-Response Demonstration
(1996)

Pavlov is a Programming By Demonstration (PBD) system that allows animated interfaces to be created without programming. Using a drawing editor and a clock, designers specify the behavior of a target interface by demonstrating stimuli (end-user actions or time) and the (time-stamped) graphical transformations that should be executed in response. This stimulus-response model allows interaction and animation to be defined in a uniform manner, and it allows for the demonstration of interactive animation, i.e., game-like behaviors in which the end-user (player) controls the speed and direction of object movement.
Contextual Equivalence for Probabilistic Programs with Continuous Random Variables and Scoring
(2017)

We present a logical relation for proving contextual equivalence in a probabilistic programming language (PPL) with continuous random variables and with a scoring operation for expressing observations and soft constraints. Our PPL model is based on a big-step operational semantics that represents an idealized sampler with likelihood weighting. The semantics treats probabilistic non-determinism as a deterministic process guided by a source of entropy. We derive a measure on result values by aggregating (that is, integrating) the behavior of the operational semantics over the entropy space. Contextual equivalence is defined in terms of these measures, taking real events as observable behavior. We define a logical relation and prove it sound with respect to contextual equivalence. We demonstrate the utility of the logical relation by using it to prove several useful examples of equivalences, including the equivalence of a βv-redex and its contractum and a general form of expression re-ordering. The latter equivalence is sound for the sampling and scoring effects of probabilistic programming but not for effects like mutation or control.
Data-Driven Vehicle Trajectory Prediction
(2016)

Vehicle trajectory or route prediction is useful in online, data-driven transportation simulation to predict future traffic patterns and congestion, among other uses. The various approaches to route prediction have varying degrees of data required to predict future vehicle trajectories. Three approaches to vehicle trajectory prediction, along with extensions, are examined to assess their accuracy on an urban road network. These include an approach based on the intuition that drivers attempt to reduce their travel time, an approach based on neural networks, and an approach based on Markov models. The T-Drive trajectory data set consisting of GPS trajectories of over ten thousand taxicabs and including 15 million data points in Beijing, China is used for this evaluation. These comparisons illustrate that using trajectory data from other vehicles can substantially improve the accuracy of forward trajectory prediction in the T-Drive data set. These results highlight the benefit of exploiting dynamic data to improve the accuracy of transportation simulation predictions.
Survey of failures and fault tolerance in cloud
(2017)

Cloud computing provides support for hosting client's application. Cloud is a distributed platform that provides hardware, software and network resources to both execute consumer's application and also to store and mange user's data. Cloud is also used to execute scientific workflow applications that are in general complex in nature when compared to other applications. Since cloud is a distributed platform, it is more prone to errors and failures. In such an environment, avoiding a failure is difficult and identifying the source of failure is also complex. Because of this, fault tolerance mechanisms are implemented on the cloud platform. This ensures that even if there are failures in the environment, critical data of the client is not lost and user's application running on cloud is not affected in any manner. Fault tolerance mechanisms also help in improving the cloud's performance by proving the services to the users as required on demand. In this paper a survey of existing fault tolerance mechanisms for the cloud platform are discussed. This paper also discusses the failures, fault tolerant clustering methods and fault tolerant models that are specific for scientific workflow applications.
Tracking the student's performance in Web-based education using Scrum methodology
(2015)

The Internet, communication and mobile technologies are emerging as predominant paradigm in the learning of students. Various technologies have created numerous opportunities for the learners to explore in the learning landscape. Even though traditional teaching and learning are still popular in India, the scope for Web based education is positive. It is commonly believed that the Web based education is poised to penetrate deep into the educational system in India in near future. While global web-based education system faces different subjects, ideas and different kind of talented student's, the biggest challenge is communication and tracking the performance of student's. To deal with this challenge, we can use scrum. Scrum is the essence and one of the powerful agile methodologies. This paper presents about scrum and scrum backlog.
Design of Incoherent Frames via Convex Optimization
(2013)

This paper describes a new procedure for the design of incoherent frames used in the field of sparse representations. We present an efficient algorithm for the design of incoherent frames that works well even when applied to the construction of relatively large frames. The main advantage of the proposed method is that it uses a convex optimization formulation that operates directly on the frame, and not on its Gram matrix. Solving a sequence of convex optimization problems allows for the introduction of constraints on the frame that were previously considered impossible or very hard to include, such as non-negativity. Numerous experimental results validate the approach.
Hyper-parameter selection in non-quadratic regularization-based radar image formation
(2008)

We consider the problem of automatic parameter selection in regularization-based radar image formation techniques. It has previously been shown that non-quadratic regularization produces feature-enhanced radar images; can yield superresolution; is robust to uncertain or limited data; and can generate enhanced images in non-conventional data collection scenarios such as sparse aperture imaging. However, this regularized imaging framework involves some hyper-parameters, whose choice is crucial because that directly affects the characteristics of the reconstruction. Hence there is interest in developing methods for automatic parameter choice. We investigate Stein’s unbiased risk estimator (SURE) and generalized cross-validation (GCV) for automatic selection of hyper-parameters in regularized radar imaging. We present experimental results based on the Air Force Research Laboratory (AFRL) “Backhoe Data Dome,” to demonstrate and discuss the effectiveness of these methods.
Automatic fusion and classification using random forests and features extracted with deep learning
(2015)

Fusion of different sensor modalities has proven very effective in numerous remote sensing applications. However, in order to benefit from fusion, advanced feature extraction mechanisms that rely on domain expertise are typically required. In this paper we present an automated feature extraction scheme based on deep learning. The feature extraction is unsupervised and hierarchical. Furthermore, computational efficiency (often a challenge for deep learning methods) is a primary goal in order to make certain that the method can be applied in large remote sensing datasets. Promising classification results show the applicability of the approach for both reducing the gap between naive feature extraction and methods relying on domain expertise, as well as further improving the performance of the latter in two challenging datasets.
Who, where, when and what: discover spatio-temporal topics for twitter users
(2013)

Micro-blogging services, such as Twitter, and location-based social network applications have generated short text messages associated with geographic information, posting time, and user ids. The availability of such data received from users offers a good opportunity to study the user's spatial-temporal behavior and preference. In this paper, we propose a probabilistic model W4 (short for Who+Where+When+What) to exploit such data to discover individual users' mobility behaviors from spatial, temporal and activity aspects. To the best of our knowledge, our work offers the first solution to jointly model individual user's mobility behavior from the three aspects. Our model has a variety of applications, such as user profiling and location prediction; it can be employed to answer questions such as ``Can we infer the location of a user given a tweet posted by the user and the posting time?" Experimental results on two real-world datasets show that the proposed model is effective in discovering users' spatial-temporal topics, and outperforms state-of-the-art baselines significantly for the task of location prediction for tweets.
Modelling the scaling properties of human mobility
(2010)

Individual human trajectories are characterized by fat-tailed distributions of jump sizes and waiting times, suggesting the relevance of continuous-time random-walk (CTRW) models for human mobility. However, human traces are barely random. Given the importance of human mobility, from epidemic modelling to traffic prediction and urban planning, we need quantitative models that can account for the statistical characteristics of individual human trajectories. Here we use empirical data on human mobility, captured by mobile-phone traces, to show that the predictions of the CTRW models are in systematic conflict with the empirical results. We introduce two principles that govern human trajectories, allowing us to build a statistically self-consistent microscopic model for individual human mobility. The model accounts for the empirically observed scaling laws, but also allows us to analytically predict most of the pertinent scaling exponents.
Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks
(2012)

Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc., have attracted millions of users to share their social friendship and their locations via check-ins. The available check-in information makes it possible to mine users’ preference on locations and to provide favorite recommendations. Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized services. To solve this task, matrix factorization is a promising tool due to its success in recommender systems. However, previously proposed matrix factorization (MF) methods do not explore geographical influence, e.g., multi-center check-in property, which yields suboptimal solutions for the recommendation. In this paper, to the best of our knowledge, we are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs. We first capture the geographical influence via modeling the probability of a user’s check-in on a location as a Multi-center Gaussian Model (MGM). Next, we include social information and fuse the geographical influence into a generalized matrix factorization framework. Our solution to POI recommendation is efficient and scales linearly with the number of observations. Finally, we conduct thorough experiments on a large-scale real-world LBSNs dataset and demonstrate that the fused matrix factorization framework with MGM utilizes the distance information sufficiently and outperforms other state-of-the-art methods significantly.
The scaling laws of human travel
(2006)

The dynamic spatial redistribution of individuals is a key driving force of various spatiotemporal phenomena on geographical scales. It can synchronize populations of interacting species, stabilize them, and diversify gene pools. Human travel, for example, is responsible for the geographical spread of human infectious disease. In the light of increasing international trade, intensified human mobility and the imminent threat of an influenza A epidemic, the knowledge of dynamical and statistical properties of human travel is of fundamental importance. Despite its crucial role, a quantitative assessment of these properties on geographical scales remains elusive, and the assumption that humans disperse diffusively still prevails in models. Here we report on a solid and quantitative assessment of human travelling statistics by analysing the circulation of bank notes in the United States. Using a comprehensive data set of over a million individual displacements, we find that dispersal is anomalous in two ways. First, the distribution of travelling distances decays as a power law, indicating that trajectories of bank notes are reminiscent of scale-free random walks known as Lévy flights. Second, the probability of remaining in a small, spatially confined region for a time T is dominated by algebraically long tails that attenuate the superdiffusive spread. We show that human travelling behaviour can be described mathematically on many spatiotemporal scales by a two-parameter continuous-time random walk model to a surprising accuracy, and conclude that human travel on geographical scales is an ambivalent and effectively superdiffusive process.
Estimating surface normals in noisy point cloud data
(2003)

In this paper we describe and analyze a method based on local least square fitting for estimating the normals at all sample points of a point cloud data (PCD) set, in the presence of noise. We study the effects of neighborhood size, curvature, sampling density, and noise on the normal estimation when the PCD is sampled from a smooth curve in R2 or a smooth surface in R3 and noise is added. The analysis allows us to find the optimal neighborhood size using other local information from the PCD. Experimental results are also provided.